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Background: Despite all the medical advances introduced for personalized patient treatment and the research supported in search of genetic patterns inherent to the occurrence of its different manifestations on the human being, the unequivocal and effective treatment of cancer, unfortunately, remains as an unresolved challenge within the scientific panorama. Until a universal solution for its control is achieved, early detection mechanisms for preventative diagnosis increasingly avoid treatments, resulting in unreliable effectiveness. The discovery of unequivocal gene patterns allowing us to discern between multiple pathological states could help shed light on patients suspected of an oncological disease but with uncertainty in the histological and immunohistochemical results. Methods: This study presents an approach for pan-cancer diagnosis based on gene expression analysis that determines a reduced set of 12 genes, making it possible to distinguish between the main 14 cancer diseases. Results: Our cascade machine learning process has been robustly designed, obtaining a mean F1 score of 92% and a mean AUC of 99.37% in the test set. Our study showed heterogeneous over-or underexpression of the analyzed genes, which can act as oncogenes or tumor suppressor genes. Upregulation of LPAR5 and PAX8 was demonstrated in thyroid cancer samples. KLF5 was highly expressed in the majority of cancer types. Conclusion: Our model constituted a useful tool for pan-cancer gene expression evaluation. In addition to providing biological clues about a hypothetical common origin of cancer, the scalability of this study promises to be very useful for future studies to reinforce, confirm, and extend the biological observations presented here. Code availability and datasets are stored in the following GitHub repository to aim for the research reproducibility: https://github.com/CasedUgr/PanCancerClassification.

Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation., Stanford Data Science and Biomedical Informatics Training Program at Stanford 2T15LM007033, Spanish Government RTI-2018-101674-B-I00 PID2021-128317OB-I00, J.de Andalucia P20-00163, Fulbright Spanish Commission, United States Department of Health & Human Services, National Institutes of Health (NIH) - USA DP2AI171011 5R25AI147369-03, German Federal Ministry for Economic Affairs and Climate Action (BMWi) BMWi 01MK21009E, Berlin Institute of Health, German Research Foundation (DFG) German Federal Ministry for Economic Affairs and Climate Action and Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation), Charite-Universitaetsmedizin Berlin

Synthetic data generation offers a solution to the data scarcity problem in biomedicine where data are often expensive or difficult to obtain. By increasing the dataset size, more powerful and generalizable machine learning models can be trained, improving their performance in clinical decision support systems. The generation of synthetic data for cancer diagnosis has been explored in the literature, but typically in the single modality setting (e.g. whole-slide image tiles or RNA-Seq data). Given the success of text-to-image synthesis models for natural images, where one modality is used to generate a related one, we propose RNA-to-image synthesis (RNA-CDM) in a multi-cancer context. First, we trained a variational auto-encoder in order to reduce the dimensions of the patient’s gene expression profile, showing that this can accurately differentiate between different cancer types. Then, we trained a cascaded diffusion model to synthesize realistic whole-slide image tiles using the latent representation of the patient’s RNA-Seq data. We show that generated tiles preserved the cell-type distribution found in real-world data, with important cell types detectable by a state-of-the-art cell identification model in the synthetic samples. Next, we successfully used this synthetic data to pretrain a multi-cancer classification model, observing an improvement in performance after training from scratch across 5-fold cross validation. Our results demonstrate the potential utility of synthetic data for developing multi-modal machine learning models in data scarce settings, as well as the possibility of imputing missing data modalities by leveraging the information present in available data modalities.

The acquisition of multi-modal biological data for the same sample, such as RNA sequencing and whole slide imaging (WSI), has increased in recent years, enabling studying human biology from multiple angles. However, despite these emerging multi-modal efforts, for the majority of studies only one modality is typically available, mostly due to financial or logistical constraints. Given these difficulties, multi-modal data imputation and multi-modal synthetic data generation are appealing as a solution for the multi-modal data scarcity problem. Currently, most studies focus on generating a single modality (e.g. WSI), without leveraging the information provided by additional data modalities (e.g. gene expression profiles). In this work, we propose an approach to generate WSI tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GAN) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists in comparison to tiles generated using traditional GANs and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles:https://rna-gan.stanford.edu/and the code for RNA-GAN is available here:https://github.com/gevaertlab/RNA-GAN.

Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.

Objective To evaluate color, lightness, chroma, hue, and translucency adjustment potential of resin composites using CIEDE2000 color difference formula. Methods Three resin composites (Filtek Universal, Harmonize, and Omnichroma) were tested. Two types of specimens were prepared: an outer base shade with an inner hole filled with test shades and single-composite specimens of all shades. Spectrorradiometric reflectances measurements and subsequent CIELAB color coordinates and translucency parameter (TP) were performed. Color (CAP00 ), lightness, chroma, hue, and translucency (TAP00 ) adjustment potential using CIEDE2000 color difference were computed. Color and transparency differences among composite materials and shades were statistically tested (P Results Positive CAP00 and TAP00 values were found for majority of tested materials. CAP00 values ranged from -0.14 to 0.89, with the highest values found for Omnichroma (>0.75 in all cases). TAP00 values ranged from -0.06 to 0.86 with significant translucency differences among dual and single specimens. Omnichroma exhibited the highest adjustment potential for all color dimensions studied. Conclusions Lightness, hue, chroma, and translucency adjustment potential have been introduced using CIEDE2000 color difference formula, and have shown their usefulness to evaluate blending effect in dentistry. Color coordinates and translucency adjustment potential were dependent on dental material. Omnichroma exhibited the most pronounced blending effect. Clinical significance Resin composites with increased color and translucency adjustment may simplify shade selection, making this process easier and less time consuming. Furthermore, these materials might facilitate challenging and complex color matching situations.

This publication is part of the R&D&I project RTI2018-101674-B-I00 and was supported by by the Government of Andalusia under grants CV20-64934 and P20-00163., Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of 96.81 +/- 1.07, an AUC of 0.993 +/- 0.004, and an AUPRC of 0.980 +/- 0.016, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient., Government of Andalusia RTI2018-101674-B-I00, CV20-64934 P20-00163

To determine the visual 50:50% perceptibility and acceptability CIEDE2000 lightness, chroma and hue human gingiva thresholds. A psychophysical experiment based on visual assessments of simulated images of human gingiva on a calibrated display was performed. A 20-obsever panel (dentists and laypersons; n=10) evaluated three subsets of simulated human gingiva: lightness subset (|ΔL'/ΔE00|≥ 0.98); chroma subset (|ΔC'/ΔE00|≥ 0.98) and hue subset (|ΔH'/ΔE00|≥ 0.96) using ΔE00 The 50:50% PT were ΔL' = 0.74 (95% confidence interval (CI) 0.39-1.09); ΔC' = 1.10 (95% CI 0.57-1.46); ΔH' = 2.40 (95% CI 1.66->3.85). The 50:50% AT were ΔL' = 2.57 (95% CI 2.00-3.06); ΔC' = 2.70 (95% CI 2.19-3.38). AT ΔH' may be considered no computable. PT values were statistically significant among the three metric differences (p ≤ 0.05). No difference was found between observers for PT values. Statistically differences in perceptual limit were found among hue, lightness and chroma for human gingiva. Thus, the observers seem to show lower sensitivity for changes in hue (ΔH') than in chroma (ΔC') and in lightness (ΔL') in the gingiva color space. PT and AT for lightness, chroma and hue specific for human gingiva should be used when evaluating natural gingiva, pink gingival shade guides or pink materials, since the thresholds of perception and acceptability for teeth are not suitable.

In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify a tumor’s metastatic potential at an early stage. While recent analyses indicated TP53 mutations as candidate biomarker, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in Whole Slide Images (WSIs) offer the potential to mitigate this issue. To assess the potential of WSIs as proxy for spatially resolved profiling or as biomarker for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. On an independent multi-focal cohort, we could show successful generalization of the model, both at patient and lesion level. Hence, the model offers insight into which lesions on a WSI most likely contain a TP53 mutation. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions. This suggests that some FP carry another alteration of which the effect converges in the same histological phenotype. Comparative expression analysis and histological cell type analysis identified such common phenotype (related to stromal composition) in both TP and FP predictions. This indicates that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations. However, we show they have the potential of capturing a tumor’s aggressive potential by observing a downstream phenotype of the tumor cells and TME associated with a biomarker of aggressive disease (TP53). Citation Format: Marija Pizurica, Maarten Larmuseau, Kim Van der Eecken, Louise de Schaetzen van Brienen, Francisco Carrillo-Perez, Simon Isphording, Nicolaas Lumen, Jo Van Dorpe, Piet Ost, Sofie Verbeke, Olivier Gevaert, Kathleen Marchal. WSI based prediction of TP53 mutations identifies aggressive disease phenotype in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB171.

Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the therapeutic resistance of glioblastoma (GBM). Studies based on single-cell RNA-seq and spatial transcriptomics have classified GBM cells into distinct transcriptional phenotypes. However, how the transcriptional diversity and spatial cellular organization are associated with patient prognosis remains incompletely resolved. Here, we developed a deep learning model to predict spatially resolved transcriptional programs from histology images. The model was trained on spatial transcriptomics data and validated in external testing cohorts. Applying the model to two separate patient cohorts led to the discovery of conserved relationships between tumor architecture and prognosis. Patient with poor prognosis had higher proportions of GBM cells expressing a hypoxia-induced transcriptional program. In addition, high clustering patterns of reactive astrocytes were associated with a poor prognosis. Conversely, when the reactive astrocytes were dispersed and connected to other cell types, the risk was decreased. To validate our results, we developed a separate deep learning model that used histology images to predict prognosis. Applying the model to spatial transcriptomics data discovered survival-associated regional gene expression programs. Genes related to glycoprotein metabolism and injury response were significantly upregulated in tumor cells with high aggressiveness. Our studies established a scalable approach to resolve the transcriptional heterogeneity of GBM and linked the spatial cellular architecture to clinical outcomes. Citation Format: Yuanning Zheng, Francisco Carrillo-Perez, Marija Pizurica, Olivier Gevaert. Spatial cellular architecture predicts prognosis in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB272.

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUA, Objective: To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods: The comprehensive review was conducted in MEDLINE/ PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions: The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance: The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures., Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00 PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, Junta de Andalucia P20-00200, Fapergs/Capes do Brasil grant 19/25510000928-3, Universidad de Granada/CBUA

Objective To evaluate the influence of neutral color backgrounds on the perception of color differences in dentistry. Methods A software was developed for this study that calculated the perceptibility (PT) and acceptability (AT) thresholds of color differences between a pair of computer-simulated incisor samples (n = 60 pairs) over three neutral color (white, gray and black) backgrounds. CIELAB and CIEDE2000 color difference formulas were used. Five groups of volunteer observers (N = 100) participated in the psychophysical experiment (n = 20): Dentists; Dental students; Dental auxiliaries; Dental technicians; and Laypersons. The psychophysical experiment was performed in a dark environment on a calibrated high-resolution screen. To determine PT and AT values, the 60 pairs of samples were randomly presented to each observer over the different backgrounds. The data were adjusted (TSK Fuzzy) and analyzed statistically using Student t-test and ANOVA (α = 0.05). Results Regardless of the metric and the background used, the PT values showed no difference among different observers (p > 0.05). Dentists showed the lowest PT values. Dental technicians showed the lowest AT (p ≤ 0.05) and similar values for the three backgrounds (p > 0.05), regardless of the metric used. The other groups of observers showed the lowest and the highest AT values when using black and white backgrounds, respectively (p ≤ 0.05). Conclusions The lowest AT values using a black background indicates that the use of this background allows for the evaluation of slight color differences, and should be used for challenging color differences in esthetic dentistry. This study showed the influence of the observer experience on color evaluation in dentistry. Clinical significance There was no influence of the background color on the perceptibility threshold. However, dentists and dental technicians showed greater ability to perceive slight color differences compared to other groups of observers.

The authors wish to thank all participants for their assistance and to Irene Cantarero-Villanueva Ph.D. and Paula Postigo-Martín, of the Department of Physiotherapy (University of Granada, Spain), for providing us with their laboratories and materials so that this study could be carried out., The mDurance® system is an innovative digital tool that combines wearable surface electromyography (sEMG), mobile computing and cloud analysis to streamline and automatize the assessment of muscle activity. The tool is particularly devised to support clinicians and sport professionals in their daily routines, as an assessment tool in the prevention, monitoring rehabilitation and training field. This study aimed at determining the validity of the mDurance system for measuring muscle activity by comparing sEMG output with a reference sEMG system, the Delsys® system. Fifteen participants were tested during isokinetic knee extensions at three different speeds (60, 180, and 300 deg/s), for two muscles (rectus femoris [RF] and vastus lateralis [VL]) and two different electrodes locations (proximal and distal placement). The maximum voluntary isometric contraction was carried out for the normalization of the signal, followed by dynamic isokinetic knee extensions for each speed. The sEMG output for both systems was obtained from the raw sEMG signal following mDurance's processing and filtering. Mean, median, first quartile, third quartile and 90th percentile was calculated from the sEMG amplitude signals for each system. The results show an almost perfect ICC relationship for the VL (ICC > 0.81) and substantial to almost perfect for the RF (ICC > 0.762) for all variables and speeds. The Bland-Altman plots revealed heteroscedasticity of error for mean, quartile 3 and 90th percentile (60 and 300 deg/s) for RF and at mean and 90th percentile for VL (300 deg/s). In conclusion, the results indicate that the mDurance® sEMG system is a valid tool to measure muscle activity during dynamic contractions over a range of speeds. This innovative system provides more time for clinicians (e.g., interpretation patients' pathologies) and sport trainers (e.g., advising athletes), thanks to automatic processing and filtering of the raw sEMG signal and generation of muscle activity reports in real-time.

Objective To evaluate the influence of neutral color backgrounds on visual thresholds using three color difference metrics: CIELAB, CIEDE2000(1:1:1) and CIEDE2000(2:1:1). Methods Sixty observers (dentists and laypersons; n = 30) from three countries participated in the study. A psychophysical experiment based on visual assessments of simulated images of teeth on a calibrated display was performed. Images of simulated upper central incisors (SUCI) were consecutively displayed in pairs (60) on 3 neutral color backgrounds (black, grey and white). Three color difference metrics (CIELAB, CIEDE2000(1:1:1), and CIEDE2000(2:1:1)) were used to calculate the visual thresholds (PT- perceptibility threshold; and AT- acceptability threshold) with 95 % confidence intervals (CI) and a Takagi-Sugeno-Kang (TSK) Fuzzy Approximation model was used as fitting procedure. Data was statistically analyzed using paired t-test (p = 0.05). Results The50:50 % PT values were significantly lower over white background than over black background. The 50:50 % AT values were significantly greater over white background than over grey and black backgrounds. In most cases, the thresholds (PT and AT) values were significantly different for each color different metric over each background color (p ≤ 0.05). No difference was found between observers for PT and AT values. Conclusions The perceptibility and acceptability thresholds in dentistry are affected by the color difference metrics and by the background color. Significance Dental color mismatches are more difficult to be accepted over a white background.